System and method for delivering a digital therapeutic from a parsed electronic message

ABSTRACT

The system and method delivers a digital therapeutic, specific to an emotional or mental state (EMS) parsed from an electronic message, comprising: an electronic computing device communicatively coupled to a processor. The processor further comprising; an EMS store; a message prescriber; a sentiment vector generator comprising: a parsing module; a coordinate-based sentiment value spectrum comprising one positive to negative-scaled axis and one perpendicular active to passive scaled axis forming a two-dimensional plot of a sentiment value along a positive to negative line (positivity correlate) and an active to passive line (activity correlate). The program executable by the processor and configured to: receive a text input comprising message content from the electronic computing device; parse, at the parsing module, the message content comprised in the text input for emotionally charged language, wherein the parsing module further comprises a semantic layer configured to recognize natural language syntax for conversion into a standardized lexicon; based on the emotionally charged language, plot a sentiment value as a point on the coordinate-based sentiment value spectrum for the text input, wherein the plotted point reflects a two-dimensional sentiment value along the two correlates of positivity and activity for said text input.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation-in-part application under 30 U.S.C.120 of U.S. patent application Ser. No. 15/702,555, filed on Sep. 12,2017. Furthermore, this application also, a continuation in part andclaims priority to U.S. application Ser. No. 15/959,075, “Delivery of aDigital Therapeutic Method and System” filed on Apr. 20, 2018 and also,a continuation in part and claims priority to U.S. application Ser. No.16/159,119 filed on Oct. 12, 2018 and further is a continuation in partof U.S. application Ser. No. 16/239,138 filed on Jan. 3, 2019.

TECHNICAL FIELD

This invention relates generally to the field of electroniccommunications and the transmittance of such communications. Morespecifically, the invention discloses a new and useful method fordelivering a digital therapeutic based on a system-mapped EMS(emotional/mental state) ascertained from parsed electronic messages.

In the past few decades, the availability and use of electroniccomputing devices, such as desktop computers, laptop computers, handheldcomputer systems, tablet computer systems, and cellular phones havegrown tremendously, which provide users with a variety of new andinteractive applications, business utilities, communication abilities,and entertainment possibilities.

One such communication ability is electronic messaging, such astext-based, user-to-user messages. Electronic messaging has grown toinclude a number of different forms, including, but not limited to,short message service (SMS), multimedia messaging service (MMS),electronic mail (e-mail), social media posts and direct messages, andenterprise software messages. Electronic messaging has proliferated tosuch a degree that it has become the primary mode of communication formany people.

While electronic messaging can be a particularly efficient mode ofcommunication for a variety of reasons—instant delivery, limitlessdistance connectivity, recorded history of the communication—electronicmessaging does not benefit from the advantages of in-personcommunication and telecommunication. For example, when communicating viatelecommunication, a person can adjust, alter, or augment the content oftheir message to an intended recipient through tone, volume, intonation,and cadence. When communicating in-person, or face-to-face, a person canfurther enhance or enrich their spoken words with eye contact and shiftof focus, facial expressions, hand gestures, body language, and thelike. In electronic messaging, users lack these critically importantsignals, clues, and cues, making it difficult for people to convey thesubtler aspects of communication and deeper intent. As a result, issuesof meaning, substance, and sentiment are often lost or confused inelectronic messages, which can, and very often does, result in harmfulor damaging misunderstandings. Miscommunications can be particularlydamaging in interpersonal and business relationships.

Another unintended effect of our overreliance on electroniccommunication is the impairment of emotional and mental health. In arecent article published in the American Journal of Psychiatry, Dr.Jerald Block wrote “technology addiction is now so common that it meritsinclusion in the Diagnostic and Statistical Manual of Mental Disorders,the profession's primary resource to categorize and diagnose mentalillnesses.” He went on to further state that the disorder leads to angerand depression when the tech isn't available, as well as lying, socialisolation and fatigue. Our devices and experiences from said devices(receiving likes, comments and shares on social media) are in essence adrug dealer and drugs, respectively: Having the capability of doling outthe same kind of dopamine hit as a tiny bump of cocaine. In effect,creating the typical addiction/dependency vicious cycle and all of theattendant consequences.

According to psychotherapist, Nancy Colier, author of “The Power ofLife”, “We are spending far too much of our time doing things that don'treally matter to us . . . [and become] disconnected from what reallymatters, from what makes us feel nourished and grounded as humanbeings.” Based on her findings, the average person checks theirsmartphones 150 times per day, or every six minutes. Furthermore, theaverage young adult sends on average 110 texts per day and 46% ofrespondents checked that their devices are something that they couldn'tlive without.

With this kind of digital ubiquity, it is becoming readily apparent thatany solution to the problem involving curtailing or augmenting userbehavior is not a realistic approach. Current approaches espoused byexperts involve any one of, or combination of, the following:Downloading an app (Moment, Alter, etc.) that locks or limits phoneusage upon reaching a pre-specified limit; disabling notifications fromyour phone settings; keeping the blue-hued light of your smartphone awayfrom your place of rest; and even buying and carrying around a dummyphone.

There is a void for a solution that takes into account ubiquitous usageand provides delivery of pro-mental and emotional healthcontent—personalized to the user, much like the way therapeutics havebecome narrowly tailored—to counter all of the digital-mediated illeffects plaguing our society. These effects will only logarithmicallygrow as we transition into the IoT era—where we will be exposed tothousands of internet-enabled objects (each capable of deliveringcontextualized analytics and provisioning) as part of our day-to-dayliving. There is a void in the market for delivering hyper-personalizeddigital-based therapeutics based on a higher-resolution assessment of anEMS (a more dynamic EMS or dEMS). A dynamic assessment based on amulti-correlate coordinate system (mood map) that allows users to plotas a single point along at least two correlates of behavior—resulting ina push of hyper-personalized digital content with therapeutic value toreinforce or counter the user-mapped dynamic assessment. Finally, thereis a void for a system-mapped EMS ascertained from parsed electronicmessages, such as a text message, email, or social media post, anddelivering digital content corresponding to the ascertained EMS.

SUMMARY

Disclosed is a method and system for imposing a dynamic sentiment vectorto an electronic message. In one embodiment of the invention, the methodcomprises: receiving a text input comprising message content from anelectronic computing device associated with a user; parsing the messagecontent comprised in the text input for emotionally-charged language;assigning a sentiment value, based on the emotionally-charged language,from a dynamic sentiment value spectrum to the text input; and, based onthe sentiment value, imposing a sentiment vector, corresponding to theassigned sentiment value, to the text input, the imposed sentimentvector rendering a sensory effect on the message content designed toconvey a corresponding sentiment.

In another embodiment of the invention, the method comprises: receivinga text input comprising message content from an electronic computingdevice associated with a user; converting the message content comprisedin the text input received from the electronic computing device intoconverted text in a standardized lexicon; parsing the converted text foremotionally-charged language; generating a sentiment value for the textinput from a dynamic sentiment value spectrum by referencing theemotionally-charged language with a dynamic library ofemotionally-charged language; and, based on the sentiment value,imposing a sentiment vector to the text input, the imposed sentimentvector rendering a sensory effect on the message content designed toconvey a corresponding sentiment.

For example, in one application of the invention, a user can write andsubmit a text message on the user's cellular phone for delivery to theuser's best friend. After receiving the text message, the invention cananalyze the message content of the text message and determine, based onthe verbiage, syntax, and punctuation within the message content, thatthe user is attempting to convey excitement through the text message.The invention can then apply a visual filter of red exclamation pointsor other illustrative, performative, or kinetic attributes to the textmessage, indicating the excitement of the user, before the text messageis delivered to the user's best friend.

In another example of one application of the invention, a user can writeand submit a direct message through a social media application (e.g.,Instagram, Facebook, SnapChat) on the user's mobile phone for deliveryto a second user. After receiving the direct message, the invention canuse a camera built into the user's mobile phone to capture an image ofthe user's face and analyze aspects of the user's face (e.g., curvatureof the lips, motion of the eyes, etc.) to determine the user's mood orexpression. Based on the user's mood or expression, the invention canthen apply a vibration pattern to the direct message before the directmessage is delivered to the second user.

In another object of the invention, sentiment and cues of the usersemotional or mental state is not gleamed by referencing a parsed userinput against a dynamic library of emotionally-charged language togenerate a sentiment value and vector for overlaying the said input.Rather, the emotional and mental state (EMS) of the user is chosen bythe user or determined by the system based on user engagement with theinterface or content. Once the EMS of the user is defined, carefullycurated and efficacious content is delivered to the user to combat thedefined EMS.

In one aspect, a method is provided for delivering a digitaltherapeutic, specific to a user-chosen emotional or mental state (EMS),the method comprising the steps of: recognizing at least one EMSselected by the user from a plurality of EMS, the selected EMSindicating at least one of a feeling, sensation, type of discomfort,mood, mental state, emotional condition, or physical status of the user.Once the EMS is defined, the method then calls for pushing aprimary-level message personalized to the user based on at least onestored message coupled to the selected EMS. Finally, pushing at least asecondary-level message personalized to the user based on athreshold-grade match of the user response to the pushed primary-levelmessage with at least one stored response coupled to a storedprimary-level message, whereby the user and stored response is a measureof at least one of a reaction, compliance, engagement, or interactivitywith the pushed and, or stored primary-level message. The primary andsecondary-level messages may contain at least one of a text, image,sound, video, art asset, suggested action or recommended behavior. Theefficaciousness or therapeutic value of the primary or secondarymessages are validated by at least one—and typically two—independentsources of clinical research or peer-reviewed science, as verified by acredentialed EMS expert.

In another aspect, once the EMS is defined, the method may call forpushing at least a single-level message. The at least single message maycontain at least one of a text, image, sound, video, art asset,suggested action or recommended behavior. Again, the efficaciousness ortherapeutic value of the primary or secondary messages are validated byat least one—and typically two—independent sources of clinical researchor peer-reviewed science, as verified by a credentialed EMS expert.

In yet another aspect, a system is described and claimed for deliveringthe digital content of validated therapeutic efficacy. The system maycomprise an EMS store; at least a primary message prescriber; aprocessor coupled to a memory element with instructions, the processorwhen executing said memory-stored instructions, configure the system tocause: at least one EMS from a plurality of EMS in the EMS store to beselected by the user, said selected EMS indicating at least one of afeeling, sensation, type of discomfort, mood, mental state, emotionalcondition, or physical status of the user; and the at least primarymessage prescriber pushing a primary-level message personalized to theuser based on at least one stored message coupled to the selected EMS.

In yet other aspects, at least a secondary message prescriber isincluded, wherein the at least secondary message prescriber pushes atleast a secondary-level message personalized to the user based on athreshold-grade match of the user response to the pushed primary-levelmessage with at least one stored response coupled to a storedprimary-level message, whereby the user and stored response is a measureof at least one of a reaction, compliance, engagement, or interactivitywith the pushed and, or stored primary-level message.

In both aspects (primary or at least secondary message prescribers), themessages or content may contain at least one of a text, image, sound,video, art asset, suggested action or recommended behavior. Much like inthe method aspects, the therapeutic value of the messages or content arevalidated by at least one—and typically two—independent sources ofclinical research or peer reviewed published science and selected by acredentialed EMS expert.

In one other aspect, a system and method for generating a more dynamicassessment allowing for more hyper-personalized digital content deliveryis provided. Users may plot as a single point on a multi-correlatecoordinate system (mood map), wherein each axis represents a uniquecorrelate of behavior and where a single point is a representation of auser-mapped assessment along at least two correlates of behavior, suchas active/inactive and positive/negative. Other reinforcing orcountering correlates may be provided. Moreover, the mood map may bethree-dimensional to include a third correlate of behavior. Finally, themood map and plotted assessment along the at least two correlates ofbehavior may be system enabled, as opposed to user-plotted. The systemmay capture emotional metrics from at least one of a facial imagecapture, heart/respiration rate, skin conductance, sensor gathered,digital footprint crawled, response to cognitive/physical tasks,engagement to pushed content, etc.

Whether the sentiment or cues are generated by the system or defined bythe user, content is being overlaid or delivered to enhance intonation,heighten digital communication, obviate ambiguity, boost mood, supportself-esteem, inspire wellness, and aid in the longitudinal andnon-interventional care for people in distress or need—leveraging afamiliar and known modality (digital devices). According to the claimedinvention, a whole ecosystem of receiving and delivering modalities areprovided for a host of digital therapeutics. The digital therapeuticofferings—with the aid of Artificial Intelligence (AI), machinelearning, and, or predictive EMS assessment tools—may deliverincreasingly personalized solutions uniquely tailored to aid eachsubscriber. Such non-interventional, anonymous, and device-centricsolutions are far more appropriate to combat the rising ill-effects ofdevice dependency—rather than pharmaceutical dosing, in-patienttreatment, and altering device behavior.

Finally, in yet another aspect, a system and method is claimed forheightening digital communication further by providing for asystem-mapped EMS ascertained from parsed electronic messages, whereinthe plotted point corresponds to a dynamic and multi-correlateassessment of behavior, emotional state, mental state, or physicalstate. The plotted point along at least two-axis of behaviors representsa more resolved assessment of the above-mentioned states and lendsitself to a more granularly-scaled EMS store with a plurality of EMS'swith their corresponding digital therapeutics. The prescriber willdeliver the digital therapeutic to a second user based on thesystem-mapped EMS either alone, before, or after delivery of theoriginal electronic message from the first user, wherein the message isdelivered either in its original state or overlaid with a sentimentvector based on the original parse.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a graphical representation of one embodiment of the electronicmessaging system.

FIG. 2 is a graphical representation of one embodiment of the electronicmessaging system.

FIGS. 3A and 3B are graphical representations of one embodiment of theelectronic messaging system.

FIGS. 4A, 4B, 4C and 4D are graphical representations of one embodimentof the electronic messaging system.

FIGS. 5A, 5B, 5C are graphical representations of one embodiment of theelectronic messaging method.

FIG. 6 is a graphical representation of one embodiment of the electronicmessaging method.

FIGS. 7A and 7B are graphical representations of one embodiment of theelectronic messaging system.

FIGS. 8A, 8B, 8C, and 8D are a flow diagrams of one embodiment of theelectronic messaging system.

FIG. 9 illustrates a network diagram in accordance with an aspect of theinvention.

FIG. 10 illustrates a block diagram depicting the digital therapeuticsystem in accordance with an aspect of the invention.

FIG. 11 illustrates a block diagram depicting the digital therapeuticsystem in accordance with an aspect of the invention.

FIG. 12 illustrates a flow diagram depicting the digital therapeuticmethod in accordance with an aspect of the invention.

FIG. 13 illustrates a representative screen shot depicting an exemplaryuser interface in accordance with an aspect of the invention.

FIG. 14 illustrates a representative screen shot depicting an exemplaryuser interface in accordance with an aspect of the invention.

FIG. 15 illustrates a representative screen shot depicting an exemplaryuser interface in accordance with an aspect of the invention.

FIG. 16 illustrates a representative screen shot depicting an exemplaryuser interface in accordance with an aspect of the invention.

FIG. 17 illustrates a block diagram representing a system including themood mapper module in relation to the EMS store and prescribers inaccordance with an aspect of the invention.

FIG. 18 illustrates a graphical representation of the mood map includingthe at least two correlates of behavior underlying the user-plottedassessment of behavior in accordance with an aspect of the invention.

FIG. 19A, FIG. 19B, FIG. 19C, and FIG. 19D are exemplary screen shots ofthe mood map interface in accordance with an aspect of the invention.

FIG. 20A and FIG. 20B are exemplary screen shots of thehyper-personalized digital therapeutic pushed to the user-plotteddynamic EMS.

FIG. 21 illustrates a method flow chart for generating thehyper-personalized digital therapeutic pushed to the user-plotteddynamic EMS.

FIG. 22 illustrates a method flow chart for delivering a digitaltherapeutic based on a parsed electronic message.

DETAILED DESCRIPTION OF DRAWINGS

Numerous embodiments of the invention will now be described in detailwith reference to the accompanying figures. The following description ofthe embodiments of the invention is not intended to limit the inventionto these embodiments but rather to enable a person skilled in the art tomake and use this invention. Variations, configurations,implementations, and applications described herein are optional and notexclusive to the variations, configurations, implementations, andapplications they describe. The invention described herein can includeany and all permutations of these variations, configurations,implementations, and applications.

In the following description, numerous specific details are set forth inorder to provide a thorough understanding of the invention. It will beapparent, however, to one skilled in the art that the invention can bepracticed without these specific details.

Reference in this specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentof the invention. The appearances of the phrase “in one embodiment” invarious places in the specification are not necessarily all referring tothe same embodiment, nor are separate or alternative embodimentsmutually exclusive of other embodiments. Moreover, various features aredescribed which may be exhibited by some embodiments and not by others.Similarly, various requirements are described which may be requirementsfor some embodiments but no other embodiments.

FIG. 1 depicts a schematic of a system 100 for imposing a dynamicsentiment vector to an electronic message. In one embodiment, a system100 can include: a sentiment vector generator 110, a processor 120, andan electronic computing device 140 associated with a particular user130. The sentiment vector generator 110, the processor 120, and theelectronic computing device 140 are communicatively coupled via acommunication network. The network may be any class of wired or wirelessnetwork including any software, hardware, or computer applications thatcan provide a medium to exchange signals or data. The network may be alocal, regional, or global communication network.

The electronic computing device 140 may be any electronic device capableof sending, receiving, and processing information. Examples of thecomputing device include, but are not limited to, a smartphone, a mobiledevice/phone, a Personal Digital Assistant (PDA), a computer, aworkstation, a notebook, a mainframe computer, a laptop, a tablet, asmart watch, an internet appliance and any equivalent device capable ofprocessing, sending and receiving data. The electronic computing device140 can include any number of sensors or components configured to intakeor gather data from a user of the electronic computing device 140including, but not limited to, a camera, a heart rate monitor, atemperature sensor, an accelerometer, a microphone, and a gyroscope. Theelectronic computing device 140 can also include an input device (e.g.,a touchscreen or a keyboard) through which a user may input text andcommands.

As further described below, the sentiment vector generator 110 isconfigured to receive an electronic message 160 (e.g., a text input)from the particular user 130 associated with the electronic computingdevice 140 and run a program 116 executed by the processor 120 toanalyze contents of the electronic message, determine a tone or asentiment that the particular user 130 is expressing through theelectronic message 160, and apply a sentiment vector to the electronicmessage 160, the sentiment vector designed to convey the tone orsentiment determined by the sentiment vector generator 110. Theelectronic message 160 can be in the form of a SMS message, a textmessage, an e-mail, a social media post, an enterprise-level workflowautomation tool message, or any other form of electronic, text-basedcommunication. The electronic message 160 may also be a transcription ofa voice message generated by the particular user 130. For example, inone embodiment, from a messaging application installed on the electroniccomputing device 140, the user 130 may select to input a voice (i.e.,audio) message through a microphone coupled to the electronic computingdevice 140 or initiate a voice message through a lift-to-talk feature(e.g., the user lifts a mobile phone to the user's ear and the messagingapplication automatically begins recording a voice message). In thisexample, the system 100 can generate a transcription of the voicemessage or receive a transcription of the voice message from themessaging application. After receiving or generating the transcription(i.e., an electronic message), the sentiment vector generator 110 canthen analyze the message content within the electronic message,determine the mood or sentiment of the message content, and apply acorresponding sentiment vector to the electronic message, as furtherdescribed below.

In one embodiment, the system 100 may receive an electronic message 160in the form of an electroencephalograph (EEG) output. For example, inthis embodiment, a user can generate a message using an electronicdevice communicatively coupled to the user and capable of performing anelectroencephalograph to measure and record the electrochemical activityin the user's brain. In this example, the system 100 can transcribe theEEG output into an electronic message 160 or receive a transcription ofthe EEG output from the electronic device communicatively coupled to theuser. After receiving or generating the electronic message 160 from theEEG, the sentiment vector generator 110 can then analyze the messagecontent within the electronic message 160, determine the mood orsentiment of the message content, and apply a corresponding sentimentvector to the electronic message. In one example of this embodiment, auser is connected to an augmented reality (AR) or virtual reality (VR)headset capable of performing an EEG or an equivalent brain mappingtechnique. The user can generate a message simply by thinking of whatthe user is feeling or would like to say. The headset can monitor andrecord these thoughts and feelings using the EEG and transcribe thethoughts and feelings into an electronic message or send the EEG outputsignals directly to the system 100. The system 100 can then analyze themessage content included within the electronic message 160, determinethe mood or sentiment of the message content, and apply a correspondingsentiment vector to the electronic message 160, creating a vectorizedmessage. The system 100 can then send the vectorized message to theuser's intended recipient (e.g., a recipient that the user thought of).

In one embodiment, the particular user 130 may submit an electronicmessage 160 through a mobile application (e.g., a native or destinationapp, or a mobile web application) installed on the particular user'smobile phone or accessed through a web browser installed on the user'sphone. In one example of this embodiment, the user accesses the mobileapplication, submits the electronic message 160 in the form of a textinput. The sentiment vector generator 110 can then analyze the messagecontent included within the electronic message 160, determine the moodor sentiment of the message content, and apply a corresponding sentimentvector to the electronic message 160, creating a vectorized message. Inthis example, the user can then send the vectorized message to theuser's intended recipient(s) 131 (e.g., by copying and pasting thevectorized message into a separate messaging application or selecting toexport the vectorized message to a separate application, as furtherdescribed below). In one variation of this embodiment, the user may sendthe vectorized message to the intended recipient 131 directly throughthe mobile application. In one embodiment, the user may submit anelectronic message 160, or a component of an electronic message (e.g., asingle word or phrase within the message content of an electronicmessage) using a touch input gesture. In one example of this embodiment,the user may submit the electronic message 160 through an electroniccomputing device by swiping a finger on a touch screen coupled to theelectronic computing device 140 in a U-shaped gesture on the electronicmessage.

In another embodiment, the user may input an electronic message 160 intoan entry field of a third-party application such as an email client(e.g., Gmail, Yahoo Mail) or a social media application (e.g., Facebook,Twitter, Instagram). For example, the user may input a message into thebody of an email, or into a status update on Facebook. In thisembodiment, the system 100 can detect the input of the electronicmessage 160 into the third-party application and upload the electronicmessage 160 to the sentiment vector generator 110. The sentiment vectorgenerator 110 can then analyze the message content contained within theelectronic message 160, determine the mood or sentiment of the messagecontent, and apply a corresponding sentiment vector to the electronicmessage 160, creating a vectorized message. The sentiment vector 110 canthen replace the electronic message 160 within the third-partyapplication with the vectorized message. Alternatively, the user mayselect to replace the electronic message 160 with the vectorized message(e.g., by copying and pasting the vectorized message into the entryfield).

FIG. 2 depicts a [schematic] of the sentiment vector generator 110. Inone embodiment, the sentiment vector generator 110 includes a parsingmodule 112, a dynamic sentiment value spectrum 114, a program 116, and alibrary of sentiment vectors. In this embodiment, after receiving anelectronic message 160, the sentiment vector generator 110 can activatethe program 116 executed by a processor 120 to analyze message contentcontained within the electronic message 160 using the parsing module112, the sentiment value spectrum 114, and the library of sentimentvectors, which are discussed in further detail below. Part or all of thesentiment vector generator 110 may be housed within the electroniccomputing device 140. Likewise, part of all of the sentiment vectorgenerator 110 may be housed within a cloud computing network.

FIG. 3 depicts a schematic of the parsing module 112. The parsing module112 is configured to parse message content contained within anelectronic message 160 received by the sentiment vector generator 110for emotionally-charged language and determine a sentiment value for theelectronic message 160 from the dynamic sentiment value spectrum 114. Inone embodiment, the parsing module 112 can include one or both of aheuristic layer 112 a and a semantic layer 112 b. The heuristic layer112 a is configured to recognize, within the message content containedwithin the electronic message 160, shorthand script, symbols, andemotional icons (emoticons). For example, the message “r u okay? :(”contains the shorthand character “r” to represent the word “are,” theshorthand character “u” to represent the word “you,” and the emoticon“:(,” representing an unhappy face, each of which the heuristic layer112 a is configured to recognize. The heuristic layer 112 a can befurther configured to translate recognized shorthand script, symbols,and emoticons into a standardized lexicon. For example, referring backto the previous example, the heuristic layer can translate “u” into“you,” “r” into “are,” and “:(” into “[sad].” The heuristic layer 112 acan thus translate the entire message from “r u okay? :(” to “are youokay? [sad]” in order to compare the sentiments expressed withindifferent messages in a more objective manner and determine the natureof the emotionally-charged language contained within the message ofcontent of the electronic message 160.

The semantic layer 112 b is configured to recognize, within the messagecontent contained within the electronic message 160, natural languagesyntax. For example, in the message “is it ok if we text on WhatsApp?”the construction of the phrases “is it ok” and “WhatsApp?” reflectnatural language syntax that can express particular sentiments. “is itok[?]” can express tentativeness in addition to the objective questionthat the phrase asks. For reference, inverting and contracting the firsttwo words to create the phrase “it's okay[?]” results in a phrase thatcan express more confidence. Likewise, the space inserted between“WhatsApp” and “?” can have the effect of “softening” the question markin comparison to “WhatsApp?” The semantic layer 112 b is configured torecognize the use of natural language syntax such as “is it ok” and“WhatsApp?” and can be further configured to translate the recognizednatural language syntax into a standardized lexicon. The standardizedlexicon can be a standard set of words and terms (e.g., an Oxforddictionary) that the parsing module 112 is able to parse foremotionally-charged language. In one embodiment, the standardizedlexicon is a standard set of words and terms with predefined attributes.For example, again referring to the previous example, the semantic layer112 b can translate the entire message from “is it ok if we text onWhatsApp?” to “can[soft] we text on WhatsApp?[soft]” in order to comparethe sentiments expressed within different messages in a more objectivemanner and determine the nature of the emotionally-charged languagecontained within the message of content of the electronic message 160.

In one embodiment, the parsing module 112 can include a library ofemotionally-charged language 112 c. In this embodiment, after parsingthe message content contained within the electronic message 160, theparsing module 112 can cross-reference the words and terms containedwith the message content to the library of emotionally-charged language112 c. The words and terms contained within the library ofemotionally-charged language 112 c may be tagged with attributesaccording to the sentiments they most commonly express. For example, thelibrary of emotionally-charged language 112 c may include the terms“disastrous,” “splendid,” “terrible,” and “awesome.” Within the libraryof emotionally-charged language 112 c, “disastrous” may be tagged withthe attribute [bad] or [negative]; “splendid” may be tagged with theattribute [good] or [positive]. In one embodiment, the terms containedwithin the library of emotionally-charged language 112 c mayadditionally or alternatively be tagged with a numeric value. Forexample, “disastrous” may be tagged with the attributes [negative; 7],and “terrible” may be tagged with the attributes [negative; 5],indicating that while “disastrous” and “terrible” may express similar“negative” sentiments, “disastrous” is more negative than “terrible.” Inone embodiment, the parsing module 112 (or, alternatively, any componentof the system 100) can dynamically add or remove words or terms to andfrom the library of emotionally-charged language 112 c. The parsingmodule 112 may use any technique to tag or evaluate the sentiments ofemotionally-charged language.

In one embodiment, the library of emotionally-charged language 112 c isspecific to the particular user 130. In this embodiment, each particularuser 130 of the system 100 access a unique library ofemotionally-charged language 112 c associated only with that particularuser. In one variation of this embodiment, the particular user 130 maymanually add or remove words and terms to and from the library ofemotionally-charged language 112 c. In one embodiment of the system 100,the system 100 can be accessed by multiple users. In one variation ofthis embodiment, the library of emotionally-charged language 112 cemployed by the parsing module 112 is the same for each user.

In one embodiment of the system 100, the parsing module additionallyincludes a neural network 150 and a library of inputs 151. In thisembodiment, after parsing the message content of an electronic message160 received by the sentiment vector generator 11, the parsing module112 can store the electronic message 160 in the library of inputs 151,along with the emotionally-charged language found within the messagecontent and any accompanying attributes, creating a database of messagesand their accompanying emotionally-charged language. In this embodiment,the neural network 150 can employ machine learning techniques to analyzethis database for patterns and trends in order to dynamically improvethe performance of the sentiment vector generator 110. For example, theneural network 150 may determine through the application of an algorithmthat the particular user 130 uses the term “disastrous” ten times moreoften than the particular user 130 uses the term “terrible.” Thus, eventhough “disastrous” may be a more negative term than “terrible” for theaverage user or person, the neural network can determine that, for theparticular user 130, “disastrous” generally carries less emotionalweight than “terrible.” In this example, the neural network 150 can thenupdate the parsing module 112 and the library of emotionally-chargedlanguage accordingly. For example, in the example in which the terms“disastrous” and “terrible” begin as tagged within the library ofemotionally-charged language 112 c as [negative; 7] and [negative; 5],respectively, the neural network 150 can update the attributes to read[negative; 5] and [negative 7], respectively. In one embodiment, theparsing module 112 can store electronic messages into the library ofinputs 151 along with their standardized lexicon conversions.

FIG. 4 depicts graphical representations of the parsing of electronicmessages by the parsing module 112. FIG. 4A depicts the parsing of threeseparate electronic messages 160, “it definitely has given me more timeand flexibility and channels creativity differently” 160 a, “is it ok ifwe text on WhatsApp?” 160 b, and “Oh u live in Williamsburg” 160 c foremotionally-charged language by the parsing module 112. In, thisexample, in the message content of 160 a, the parsing module 112determines three emotionally-charged words and terms: “definitely has,”“and,” and “differently;” in the message content of 160 b: “ok,” “we,”and “WhatsApp?” and in the message content of 160 c: “u” and“Williamsburg.” In one embodiment, as discussed above, after parsing themessage content, the parsing module 112 can determine attributes for theemotionally-charged language found in the message content, as depictedby S123 in FIG. 4B. In the example depicted in FIG. 4B, the parsingmodule 112 tags “definitely has” with [positive, active], “and” with[neutral], and “differently” with [negative]. In one embodiment, asdiscussed above, the parsing module 112 includes a semantic layer 112 bconfigured to recognize, within the message content contained within theelectronic message 160, natural language syntax, as depicted by S122 inFIG. 4B. In the example depicted in FIG. 4B, the semantic layer 112 brecognizes the space between “WhatsApp” and “?” in “is it ok if we texton WhatsApp?” as an instance of natural language syntax. In oneembodiment, as discussed above, the parsing module 112 includes aheuristic layer 112 a configured to recognize, within the messagecontent contained within the electronic message 160, shorthand script,symbols, and emoticons, as depicted by S124 in FIG. 4B. In the exampledepicted in FIG. 4B, the heuristic layer 112 a recognizes “u” as ashorthand term for “you.”

In one embodiment, as discussed above, after parsing the message contentcontained within the electronic message 160, the parsing module 112 cancross-reference the words and terms contained with the message contentto a library of emotionally-charged language 112 c, as depicted in FIG.4C. In the example depicted in FIG. 4C, the parsing module 112cross-references electronic message 160 a with the library ofemotionally-charged language 112 c and determines that “differently,”“more,” “flexibility,” and “differently” are emotionally-charged wordsor terms. In one embodiment, as discussed above, before parsing themessage content of an electronic message 160, the parsing module 112 canconvert the message content into a standardized lexicon, as depicted inFIG. 4D. In the example depicted in FIG. 4D, the parsing module 112converts “is it ok if we text on WhatsApp?” into the converted text, “isit okay if we text on WhatsApp?” in step S126 before parsing theconverted text for emotionally-charged language in step S128.

FIG. 5A-5C depicts a graphical representation of a dynamic sentimentvalue spectrum 114. In one embodiment, after parsing message content ofan electronic message 160 for emotionally-charged language, thesentiment vector generator 110 can generate a sentiment value from adynamic sentiment value spectrum 114 for the electronic message 160. Inone variation of this embodiment, the dynamic sentiment value spectrum114 can be represented as a coordinate system, as depicted in FIG. 5A.In the example depicted in FIG. 5A, the dynamic sentiment value spectrum114 is a Cartesian coordinate system consisting of two axes: ahorizontal axis 115 a ranging from positive to negative (henceforth, thepositivity axis) and a vertical axis 115 b ranging from passive toactive (henceforth, the activity axis). In this example, the dynamicsentiment value spectrum 114 consists of a multitude of differentsentiments, each occupying a different position on the coordinatesystem. For example, the sentiments “Happy,” “Astonished,” and“Inquisitive” (114 a-114 c, respectively) all occupy the second quadrantof the coordinate system, defined by a positive position on thepositivity scale and an active position on the activity scale (i.e.,each of these sentiments are determined by the sentiment vectorgenerator 110 to be positive and active sentiments). In this example,the sentiment vector generator considers Inquisitive 114 c to be a moreactive but less positive sentiment than Astonished 114 b and Astonishedto be a less positive and less active sentiment than Happy 114 a. Also,in this example, the sentiments “Shocked,” “Sad,” and “Mad” (114 d-114f, respectively) all occupy the first quadrant of the coordinate system,defined by a negative position on the positivity scale and an activeposition on the activity scale (i.e., each of these sentiments aredetermined by the sentiment vector generator to be active and negativesentiments). However, the dynamic sentiment value spectrum 114 need notbe a coordinate system. Rather, the dynamic sentiment value spectrum 114may take on any appropriate form (e.g., a list, a linear scale, etc.).Additionally, the sentiment value spectrum does not need to be dynamic.

In one embodiment, as discussed above, after parsing message contentcontained within an electronic message 160 for emotionally-chargedlanguage, the parsing module 112 can assign attributes to theemotionally-charged language found in the message content of theelectronic message 160. In one embodiment, the sentiment vectorgenerator 110 can analyze the emotionally-language and theiraccompanying attributes to generate a sentiment value from the dynamicsentiment value spectrum 114, as depicted in FIG. 5B. For example, inthe example depicted in FIG. 5B, the parsing module 112 can assign eachemotionally-charged term found in the message content of an electronicmessage with respective coordinate values on the positivity and activityaxes of the Cartesian coordinate dynamic sentiment value spectrumdiscussed in the example above. In this example, the sentiment vectorgenerator 110 can take the coordinate position of eachemotionally-charged term, calculate an average position of theemotionally-charged terms, and plot the average positon on the dynamicsentiment value spectrum 114 depicted in FIG. 5A. Then, in this example,the sentiment vector generator 110 can generate a sentiment value forthe electronic message by determining the sentiment value on the dynamicsentiment value spectrum 114 closest to the average position of theemotionally-charged terms.

In one embodiment, the sentiment vector generator 110 can generate asentiment value for an electronic message 160 by determining which ofthe emotionally-charged terms found in the message content of theelectronic message carries the most emotional weight. For example, inone embodiment, the parsing module 112 can parse the message content ofan electronic message 160 for emotionally-charged language and assigneach emotionally-charged term with a positivity scale value, an activityscale value, and an emotional weight value. In this embodiment, thesentiment vector generator 110 can then determine a sentiment value forthe electronic message by determining which of the emotionally-chargedterms has the highest emotional weight value, and then determining thesentiment value on the dynamic sentiment value spectrum 114 closest tothe position of emotionally-charged term with the highest emotionalweight value.

In one embodiment, the library of emotionally-charged language 112 cassociates each emotionally-charged term contained within the librarywith a sentiment value from the dynamic sentiment value spectrum 114.For example, the library of emotionally-charged language 112 c mayassociate the words “gleeful,” “splendid,” and “terrific” with a “happy”sentiment value. In this example, if the message content of anelectronic message 160 includes any of the terms “gleeful,” “splendid,”or “terrific,” the sentiment vector generator 110 can generate a “happy”sentiment value for the electronic message 160. However, the sentimentvector generator can generate a sentiment value for an electronicmessage 160 using any other methodology.

In one embodiment, the particular user 130 may select a sentiment valuefrom the dynamic sentiment value spectrum for an electronic message 160.In one variation of this embodiment, after the parsing module 112 parsesthe message content of an electronic message 160 submitted by theparticular user 130, the sentiment vector generator 110 can generatemultiple sentiment values for the electronic message 160 and present themultiple sentiment values for the electronic message 160 to theparticular user 130 for selection. For example, after receivingelectronic message 160 a (depicted in FIG. 4A), the sentiment vectorgenerator 110 may generate an “excited” sentiment value and a“melancholy” sentiment value for electronic message 160 a. In thisexample, the particular user 130 may be given the choice to pick betweenthe “excited” sentiment value and the “melancholy” sentiment value, inorder to further ensure that the proper (i.e., intended) sentiment willbe expressed.

In one embodiment, as discussed above, the system 100 includes a neuralnetwork 150 and a library of inputs 151 communicatively coupled to thesentiment vector generator 110. In one variation of this embodiment,after generating a sentiment value for an electronic message 160, thesentiment vector generator 110 store the electronic message 160 and itsaccompanying sentiment value in the library of inputs 151 creating adatabase of messages and their accompanying sentiment values. In thisembodiment, the neural network 150 can employ machine learningtechniques to analyze this database for patterns and trends in order todynamically improve the performance of the sentiment vector generator110. In one variation of this embodiment, the neural network 150 candynamically edit or rearrange the dynamic sentiment value spectrum 114.In the rearranged version, the sentiment values have adjusted andcoalesced into more discrete sections. This may reflect that aparticular user 130 associated with the rearranged sentiment valuespectrum 117 generates messages most of their messages with a similartone, making the difference between similar sentiments subtler than thatof the average person.

In one embodiment, the sentiment vector generator 110 can generate asentiment value for an electronic message 160 at least in part byutilizing information about a particular user 130. For example, in oneembodiment, the system 100 can generate sender context associated with aparticular user 130. The sender context can include, but is not limitedto: social media data associated with the particular user, data obtainedfrom IoT (internet of things) devices associated with the particularuser, data obtained from wearable devices associated with the particularuser, genetic profile data associated with the particular user, andstress data of the particular user. In one variation of this embodiment,the system 100 can leverage sensors and inputs coupled to an electroniccomputing device 140 associated with the particular user 130 to generatesender context associated with the particular user 130, as depicted bystep S160 in FIG. 6. For example, in the example depicted in FIG. 6, thesystem 100 can leverage a camera built into a mobile phone associatedwith the particular user 130 to capture images of the face of theparticular user. In this example, the system 100 can then analyze theimages of the face of the user (e.g., the eye motion or lip curvature ofthe user) and determine the mood of the user at the time that theelectronic message 160 is generated. The sentiment vector generator 110can then generate a sentiment value using the determined mood of theuser. In one variation of this embodiment, the system 100 can leveragesensors coupled to wearable devices associated with a particular user,such as a smart watch, intelligent contact lenses, or cochlear implants.For example, the system 100 can leverage a microphone built into acochlear implant to capture the heart rate of a user at the time thatthe user is generating an electronic message 160. Using the capturedheart rate, the sentiment vector generator 110 can then determine astress level of the user at the time that the user generated theelectronic message 160 and generate a sentiment value using thedetermined stress level of the user. Sender context can additionally oralternatively include: facial expression, motion or gesture, respirationrate, heart rate, and cortisol level.

In another variation of the previous embodiment, the sentiment vectorgenerator 110 can generate a sentiment value for an electronic message160 at least in part by utilizing information about an intendedrecipient of the electronic message 160. In this embodiment, afterreceiving an electronic message 160, the system 100 can determine anintended recipient 131 of the electronic message 160. The system 100 canthen generate recipient context associated with the intended recipient131. The recipient context can include but is not limited to: socialmedia data associated with the intended recipient, data obtained fromIoT (internet of things, e.g., a smart home assistant such the AmazonEcho) devices associated with the intended recipient, data obtained fromwearable devices associated with the intended recipient, genetic profiledata associated with the intended recipient, and stress data associatedwith the intended recipient. For example, in one embodiment, the system100 can leverage sensors built into an electronic device 141 associatedwith the intended recipient to determine a mood of the intendedrecipient 131 at the time that the electronic message 160 is generated.The sentiment vector generator 110 can then generate a sentiment valuefor the electronic message 160 based at least in part on the determinedmood of the intended recipient 131.

After generating a sentiment value for an electronic message 160, thesentiment vector generator 110 can then select a sentiment vector from alibrary of sentiment vectors 118, the selected sentiment vector designedto convey a sentiment corresponding to the generated sentiment value,and impose the selected sentiment vector to the electronic message 160,as depicted in FIG. 7. The library of sentiment vectors 118 can includebut is not limited to: a color change of a component of the messagecontent, a change in the text font of a component of the messagecontent, an audio effect, a haptic effect, and a graphical addition tothe message content. For example, in one embodiment, after generating a“mad” sentiment value, the sentiment vector generator 110 may change thebackground of the electronic message 160, as depicted by step S141 a inFIG. 7A, such as changing the background of the electronic message 160to red to reflect the mad sentiment. Or, for example, in one variationof this embodiment, the sentiment vector generator 110 may opt tohighlight only key words or terms in red, or change the fonts of keywords or terms to red. The sentiment vector generator 110 can impose anysort of color change to the electronic message 160 in order to convey acorresponding sentiment.

In one embodiment, for example, after generating an “inquisitive”sentiment value for an electronic message 160, the sentiment vectorgenerator 110 may impose a graphic onto the electronic message 160, asdepicted by step 141 b in FIG. 7A, such as adding question mark graphicsto the background of the electronic message 160. In one variation ofthis example, the sentiment vector generator 110 can add one questionmark to the end of the message content of the electronic message 160 ina font size that is larger than the font size of the rest of the messagecontent. In another variation of this example, the sentiment vectorgenerator 110 may impose a .gif file to the background of electronicmessage 160, in which one question mark grows and shrinks in periodicintervals. The sentiment vector generator 110 can impose any sort ofstatic or dynamic graphic to the electronic message 160 in order toconvey a corresponding sentiment.

In one embodiment, for another example, after generating a “judgmental”sentiment value for an electronic message 160, the sentiment vectorgenerator 110 can edit the font of a key word in the message content, asdepicted by step S141 c in FIG. 7A, such as italicizing one of the wordscontained in the message content. Such font effects can include, but arenot limited to, italicizing the font, changing the size of the font,bolding, underlining, and changing the spacing between characters,words, and lines. The sentiment vector generator 110 can impose any sortof font change to the electronic message 160 in order to convey acorresponding sentiment.

In one embodiment, the sentiment vector generator 110 can impose ananimated character or personality to the electronic message 160, ortranspose the electronic message 160 into a graphic of an animatedcharacter or personality. For example, in one variation of thisembodiment, the library of sentiment vectors 118 may include a series ofthe same animated character (take, for example, an animated llama orchicken) performing various actions associated with variouscorresponding sentiments. For example, the library of sentiment vectors118 may include a static or dynamic graphic of an animated chickenstomping with red eyes (expressing anger), another graphic of theanimated chicken laying in a hammock and basking in the sun (expressingcontentedness), and another graphic of the animated chicken blowing akiss (expressing affection). In this example, after generating an“anger” sentiment value for an electronic message 160, the sentimentvector generator 110 can transpose the electronic message into thegraphic of the animated chicken stomping and saying the message contentof the electronic message 160.

In one embodiment, the sentiment vector generator 110 can impose ahaptic effect onto an electronic message 160. For example, aftergenerating an “anger” sentiment value for an electronic message 160, thesentiment vector generator 110 can impose a vibration or vibrationpattern onto the electronic message 160, as depicted by step S141 d inFIG. 7B, such as three short vibrations. In another example, aftergenerating a “contented” sentiment value for an electronic message 160,the sentiment vector generator 110 can impose one long and mutedvibration to the electronic message 160. The sentiment vector generator110 can impose any form of vibration or vibration pattern to anelectronic message in order to convey a corresponding sentiment.

In one embodiment, the sentiment vector generator 110 can impose anaudio effect onto an electronic message 160. For example, aftergenerating an “unhappy” sentiment value for an electronic message 160,the sentiment vector generator 110 can impose an audio accompanimentonto the electronic message 160, as depicted by step S142 in FIG. 7B,such as protracted “nooo.” In another example, the sentiment vectorgenerator 110 can impose a voice accompaniment dictating the messagecontent of the electronic message 160 and stressing key words containedwithin the message content. The voice accompaniment may stress key wordscontained within the message content in any number of ways including,but not limited to: increasing or decreasing in volume, changing theintonation of the voice, changing the speed of the voice, or changingthe cadence of the voice accompaniment. In one embodiment, the voiceaccompaniment vector may be a recorded and processed version of theparticular user's voice. In one embodiment, the voice accompanimentvector may be the voice of another individual, such as a celebrity, or acombination of the particular user's voice and the voice of anotherindividual.

In one embodiment, after generating a sentiment value for an electronicmessage 160, the sentiment vector generator 110 can impose a vector ontothe electronic message 160 that adjusts the position of the wordscontained with the message content of the electronic message, asdepicted by step S141 e in FIG. 7B. In one variation of this embodiment,the adjustment of the words contained within the message content isstatic, such that the words occupy new positions in a static image. Inone variation of this embodiment, the adjustment of the words containedwithin the message content is dynamic, such that the words containedwithin the message content move within the resulting vectorized message.

In one embodiment, a user may submit sentiment vectors to the sentimentvector generator 110. For example, in one embodiment, a user may submita picture or graphic design to impose onto the background of anelectronic message and select a sentiment value for the picture orgraphic design to be associated with. In this example, after generatinga sentiment value for an electronic message 160 corresponding to thesentiment value that the user has selected to associate with the pictureor graphic design, the sentiment vector generator 110 can impose thepicture or graphic design to the background of the electronic message160 to convey the corresponding sentiment. In another example, in onevariation of this embodiment, a user can select a sentiment vectorpreviously included in the library of sentiment vectors 118 andpreviously associated with a sentiment value and disassociate thesentiment vector from the associated sentiment value, or re-associatethe sentiment vector with a different sentiment value. In yet anotherexample, in one variation of this embodiment, a user can select one ormore elements from existing sentiment vectors contained within thelibrary of sentiment vectors 118 and combine them to create a newsentiment vector. In this example, the user can also choose a sentimentvalue to associate with the new sentiment vector. In another example, inone variation of this embodiment, a user can select a sentiment vectorby scrolling through a list of sentiment vectors (e.g., a list includingoptions to adjust text weight, height, font, color, highlight, orcontent animation) using a flicking gesture, within a mobileapplication, on a touch screen coupled to an electronic computingdevice.

The sentiment vector generator can include or generate, but is notlimited to, sentiment vectors using any combination of the elements ofthe sentiment vectors described herein. Additionally, environmentalconditions and factors for example, but not limited to, wind, heat,humidity, cold may also play a role in generating the sentiment vector.

In one embodiment of the system 100, a user can submit an electronicmessage 160 to the sentiment vector generator 110 through a mobileapplication (e.g., a native application), as discussed above. In onevariation of this embodiment, the mobile application can storevectorized messages generated by the sentiment vector generator andallow the user to search through the vectorized messages. In thisembodiment, the user can search through the vectorized messages usingdifferent filters or queries including, but not limited to: mood, color,content, and sentiment. For example, in one embodiment, the user canenter a sentiment as “anger” as a search query, and a graphical userinterface of the mobile application can display a list of all of thevectorized messages that the user has created through the sentimentvector generator 110 with a sentiment value corresponding to an “anger”sentiment. In one embodiment, the sentiment vector generator 110 canimpose a hyperlink onto an electronic message 160. FIGS. 8A, 8B, 8C, and8D are flow diagrams of one embodiment of the electronic messagingsystem.

In an embodiment of the invention, the sentiment vector generator 110can impose a hyperlink onto an electronic message 160. An imperativefunction of the sentiment vector is GEEQ (genetics, emotion andelectroencephalography) and its capacity to integrate messages andmessaging with movement and thought as well as the ability to pairinformation with form and performative elements. In a nutshell, ourtechnology will introduce, integrate, account for, and actively utilizeGEEQ (Genetics, Emotion, and Electroencephalography). GEEQ, by its verydesgn, integrates and intermingles the beliefs and postulates of Darwin,Mendel, Mendelssohn, Morgan, and Martha Graham.

FIG. 9 illustrates a network diagram of the digital therapeutic systemin accordance with an aspect of the invention. As shown, at least oneprocessor 204 is connected to the Internet (network) 206 via either awireless (e.g. WiFi link) or wired link to an Internet connected router,usually via firewall. The network 206 may be any class of wired orwireless network including any software, hardware, or computerapplications that can provide a medium to exchange signals or data. Thenetwork 206 may be a local, regional, or global communication network.Various servers 204, such as a remote VCS Internet server, andassociated database memory can connect with the at least a user device(1 . . . n). Additionally, various user devices (e.g. Smartphones,tablet computers, laptop computers, desktop computers and the like) canalso connect to both the processor-controlled IoT hubs, sensors disposedon the device configured for data gathering, and/or the remote VCSInternet server 204.

As will be discussed, often a plurality of different user devices may beused, but for simplicity this plurality of devices will often be spokenof in the singular form. This use of the singular form is not intendedto be limiting, and in general the claims and invention should beunderstood as operating with a plurality of devices. Although forsimplicity, often mobile client computerized devices such as Internetconnected versions of the popular Android, iOS, or Windows smartphonesand tablets will be used as specific examples of devices, these specificexamples are not intended to be limiting. The electronic computingdevice may include any number of sensors or components configured tointake or gather data from a user of the electronic computing deviceincluding, but not limited to, a camera, a heart rate monitor, atemperature sensor, an accelerometer, a microphone, and a gyroscope. Theelectronic computing device can also include an input device (e.g., atouchscreen or a keyboard) through which a user may input text andcommands.

While not shown, note that server, Internet connected storage device anddatabase memory may all be located in the cloud. This is intended toboth designate and remind the reader that the server, Internet connectedstorage device and database memory are in fact operating according toscalable Internet cloud-based methods that in turn operate according toautomated service provisioning and automated virtual machine migrationmethods. As previously discussed, examples of such scalable methodsinclude, but are not limited to, Amazon EC2, Microsoft Windows Azureplatform, and the Google App Engine. Thus, for example, server andInternet connected storage device will often be implemented asautomatically provisioned virtual machines under a cloud service systemthat can create a greater or lesser number of copies of server andInternet connected video storage device and associated database memoryaccording to the underlying demands on the system at any given time.

Preferred embodiments may include the addition of a remote server 204 orcloud server to further provide for back-end functionality and support.Any one of the storage or processing may be done on-board the device orbe situated adjacent or remotely from the system and connected to eachsystem via a communication network 206. In one embodiment, the server204 may be used to support user behavior profiling; user historyfunction; predictive learning/analytics; alert function; network sharingfunction; digital footprint tracking, etc. The remote server 204 may befurther configured to authenticate the user and retrieve data of theuser, device, and, or network and applies the data against a library ofmessages, content, validated user information, etc.

Now in reference to FIGS. 10 and 11. FIGS. 10 and 11 both illustrate anexemplary embodiment of the digital therapeutic delivery system. FIGS.10 and 11 illustrate an exemplary processing unit with at least a oneprescriber 305, 307 configured for displaying interactively therapeuticcontent from an EMS store 303, 403 based on a user-specific EMS. Asshown, the system may comprise an EMS store 303, 403; at least a primarymessage prescriber 305; a processor coupled to a memory element withinstructions, the processor when executing said memory-storedinstructions, configure the system to cause: at least one EMS from aplurality of EMS in the EMS store 303, 403 to be selected by the user.

Any number of EMS or EMS types may be included in the EMS store 303,403. Each EMS may indicate at least one of a feeling, sensation, type ofdiscomfort, mood, mental state, emotional condition, physical status ofthe user, and, or a behavioral intervention or training regimen. Anynumber of messages or interactively therapeutic content may beassociated with each EMS type. Each message; or interactivelytherapeutic content; or pushed therapeutic may contain at least one of atext, image, sound, video, art asset, suggested action or recommendedbehavior. The matching of message; interactively therapeutic content; orpushed therapeutic with EMS type may be pre-defined by at least one ofan accredited expert or source; probabilistic; or deep learned. In apreferred embodiment, an accredited expert or source will require atleast two independent sources of peer-reviewed scholarship or data inorder to validate the match.

The at least primary message prescriber 305 may push a message orinteractively therapeutic content personalized to the user based on atleast one stored message matched to the selected EMS. For example,within the EMS store 403, if EMS 1 (lethargic) is selected as defined bythe user or the system, any one of message 1, 2 . . . n may be selectedby the prescriber 305. The pre-defined messages validated by theaccredited expert may all be messages with documented utility inelevating mood and energy (rubric). The mood and energy documented foreach message may be on a scale. For instance, EMS 1 message 1 may below-moderate; EMS 1/message 2 may be moderate; and EMS 1/message n maybe high-severe, etc. Any variant of the scale may be featured withoutdeparting from the scope of the invention. In other embodiments, themessages, while falling under the same rubric and un-scaled, can varyalong design cues. For instance, the prescriber 305 may choose EMS1/message 2, over other available messages, due to the fact that themessage is comprised of traditionally feminine cues (pink-coloredbauhaus typeface) for a female user. Other user profile or demographicinformation may further inform the prescribers 305 choice of messagetype, such as age, education level, voting preference, etc. User profileor demographic information may be user inputted or digitally crawled.

Still in reference to FIGS. 10 and 11, the prescriber's 305 choice ofmessage type is not specific to a user, user profile, or crawled userdata. In a certain embodiment, the prescriber 305 may have to choosebetween any one of the message types (message 1, message 2 . . . messagen) from the selected EMS type. This type of message assignment may becompletely arbitrary. In other embodiments, the message assignment maybe not specific to a user-generated or crawled profile but may be basedon user history. In other words, a user's tracked level of engagementwith a previous message or message from a previous session may informmessage assignment by the prescriber 305. Tracking engagement of a userwith a pushed or prescribed therapeutic message may be bycamera-captured eye gazing, touch-screen interaction, time span betweenpushed therapeutic and user follow-up action, choice of follow-upaction, etc.

In some embodiments, the full list of message types is not grouped byEMS type or along any design categories, but rather simply listedarbitrarily and mapped or matched to an appropriate EMS type. In thisarbitrarily listed manner, the prescriber 305 may match to more than oneEMS type. Likewise, a user may be defined by more than one EMS type andbe prescribed the same message type.

FIG. 12 illustrates a flow diagram depicting the method of delivering adigital therapeutic in accordance with an aspect of the invention. In apreferred embodiment, the method may comprise the steps of: (1)recognizing at least one EMS selected by the user from a plurality ofEMS, the selected EMS indicating at least one of a feeling, sensation,type of discomfort, mood, mental state, emotional condition, or physicalstatus of the user 508. Once the EMS is defined, the method then callsfor (2) pushing at least a primary-level message personalized to theuser based on at least one stored message coupled to the selected EMS509.

In some embodiments, the system or method may call for pushing at leasta secondary-level message personalized to the user based on athreshold-grade match of the user response to the pushed primary-levelmessage with at least one stored response coupled to a storedprimary-level message, whereby the user and stored response is a measureof at least one of a reaction, compliance, engagement, or interactivitywith the pushed and, or stored primary-level message. Much like theprimary message or primary-level message, the secondary-level messagesmay also contain at least one of a text, image, sound, video, art asset,suggested action or recommended behavior. Again, the efficaciousness ortherapeutic value of the primary or secondary messages are validated byat least one—and typically two—independent sources of clinical researchor peer-reviewed science, as verified by a credentialed EMS expert.

In order to facilitate the at least secondary message or secondary-levelmessage, the primary prescriber 305 may be used: Assigning a secondmessage to the same user in the same session for the first defined EMStype. As is with the the assignment of the first message, the assignmentof the second may arbitrarily choose among EMS-grouped messages or fromthe full arbitrary list of messages in the EMS store. Moreover, theprimary prescriber 305 may perform the secondary assignment in alogic-defined manner, wherein gathered, contextualized, or profiled datainforms the assignment. In yet other aspects, second-level assignmentmay be performed by at least a secondary message prescriber 307, whereinthe at least secondary message prescriber 307 pushes at least asecondary-level message personalized to the user based on athreshold-grade match of the user response to the pushed primary-levelmessage with at least one stored response coupled to a storedprimary-level message, whereby the user and stored response is a measureof at least one of a reaction, compliance, engagement, or interactivitywith the pushed and, or stored primary-level message.

For instance, when a user-generated or system-generated EMS is definedas ‘unfulfilled’ for user A, a primary prescriber 305 assigns message 2(uplifting; inspiring message) from EMS 1 (unfulfilled). In oneembodiment, a secondary prescriber 307 prescribes a pro-social behavior,such as a local community service, immediately upon a touch interactionwith the first inspiring message pushed. In other embodiments, a levelof engagement, interaction or compliance may be tracked by the system toinfer severity of the EMS. For instance, if user A does not comply withthe touch-interaction requests from the first inspiring message orpro-social behavior recommendation of the second message, then thesecondary prescriber 307 may push a less physically strenuous pro-socialrecommendation, such as suggesting to call an in-network licensed expertor simply make a cash donation to a charitable organization of the userschoosing via a linked micro-payment method. For the purposes ofinferring severity of EMS, any number of diagnostics that leverage anyone of the on-device tools may be used, such as gyroscopic sensors orcameras. Secondary assignment may also be based on learned history, suchas a past positive reaction (compliance) to a receiving a message from aloved one that a donation was made in user A's name to a charitableorganization. Based on such history, a secondary prescriber 307 mayassign a primary or secondary message recommending to make a donation inthe name of a loved one during an ‘unfulfilled’ EMS experienced by userA.

The processing unit may further be communicatively coupled to at leastone of an interface module, display module, input module, logic module,a context module, timeline module, tracking module, notification module,and a payment/gifting module. In accordance with one aspect, thenotification module may be configured to generate reports at regularintervals (such as daily at 12:00 PM, weekly and monthly), on-demand(when the user requests for a report corresponding to the user), whentriggered by an event, or upon a detected severe EMS. In an embodimentof the present invention, the notification module may also be configuredto send a notification to the user or to a chosen loved one of the user.The notification may be a message, a phone call or any othercommunication means.

In an embodiment of the present invention, a timeline module may pushalready pushed messages in at least one of a static, dynamic, and, orscheduled fashion based on at least one of the user's schedulercriteria. The line of static, dynamic, and, or scheduled messages may becurated by the user, pre-set, or dynamically pushed based on any one ofa user parameter. In some embodiments, the timeline module enables thedisplayed line of static, dynamic, and, or scheduled messages to befurther replicated on at least one of a social media timelines orstories. In other words, the timeline module enables the displayedmessages to be further shared with social media outlets.

In an embodiment of the present invention, a payment or gifting modulemay enable purchasing and gifting donations, physical objects, ordigital assets. The gifting module may further be coupled to adistributive digital ledger, wherein each transaction among any user isrepresented as a unique node in the digital ledger. Each node taggedwith meta data facilitating at least one of a transaction, validationand, or registration for each transaction.

FIG. 13 is a representative screen shot depicting an exemplary userinterface in accordance with an aspect of the invention. As shown, thetop layer 602 depicts a spotlighted EMS and the bottom layer is a scrollmenu of EMS. In this case, the concept of EMS, as earlier defined, alsoincludes behavioral interventions or training regimens, in addition toan emotional and mental state. In some embodiments, an exemplary userexperience may have both top layer 602 and bottom layer 604 within thesame screen, wherein the top layer 602 is a spotlighted rendering of thefocused EMS from the EMS menu depicted in the bottom layer 604. In otherembodiments, the window may only feature the scrolling EMS menu asdepicted in the bottom layer 604, wherein the focused EMS from theplurality of EMS may pop-out, or be emphasized anyhow. In yet otherembodiments, the window may only feature the one EMS at a time, allowingfor the user to go through the entire menu, one window (EMS) at a time.In yet other embodiments, the menu may be featured in a thumbnailformat, allowing the user to choose at least one EMS from a thumbnailmenu, sized to fit in a single window, or alternatively, configured forscrolling.

FIG. 14 is a representative screen shot depicting an exemplary userinterface in accordance with an aspect of the invention. Once the EMS(behavioral intervention or training regimen) is defined, users can readmore about the intervention or training regimen they're going to startand self-administer (have pushed to their device) from a top portion ofthe card (window) 702. On the same card (window), the bottom portion mayhighlight proven benefits, and then provide directions for use, mixingreal guidance with elements of humor 704. The medical-inspiredalliteration and iconography are intended to invoke a sense ofprescriptive health care or wellness.

FIGS. 15 and 16 are representative screen shots depicting an exemplaryuser interface in accordance with an aspect of the invention. As shown,once the EMS (regimen) is defined and a particular course of treatment(message) is started, on the top-right portion of the next cardexplicitly identifies the specific drug benefit 802. While not shown, bytapping the drug abbreviation, users can see the source of supportingscientific research 802. By tapping the hamburger icon, users can chooseto save the individual card, or share the card and its contents withfriends across social media. It is to be understood by a person ofordinary skill in the art that these icons, or any icons, on this card(window), or any card (window), may be positioned elsewhere (oranywhere), without departing from the inventive scope.

The focal point of the card (window) is the actual EMS-defined message(treatment), and in the case of this window, is a suggested action—jumpfor 5 seconds. Jumping for 5 seconds is a suggested action to restorethe oxytocin neurotransmitter, which is documented for buildinghappiness and confidence—the initially chosen EMS or behavioralintervention by the user (FIG. 14). The veracity of the message orsuggested action is supported by the referenced peer-reviewed researchand co-signed credentialed expert 802. As a person, skilled in the artwill recognize from the previous detailed description and from thefigures and claims, modifications and changes can be made to theembodiments of the cards, windows, icons, design elements, EMS types,behavioral intervention types, message types, without departing from thescope of this invention as defined in the following claims.

While not shown in FIG. 16, the messages (cards/windows) may comprise asingle or battery of physical and, or cognitive tasks and based onresponses, further indicate a more nuanced EMS for a more tailoredinitial or subsequent message. Responses may include a level ofcompliance, engagement, interaction, choices, etc. Furthermore, fordeeper and more nuanced EMS definition, assigning an indication score orcolor-coded range to further convey EMS severity may be achievable. As aresult, matching of message type to scored or color-coded EMS mayproduce a more refined match for pushing of even more personalizeddigital content or therapeutics.

The claimed invention leverages existing clinical research and provenscience (already published in peer-reviewed journals) and repackagesinsights as content modules or behavioral interventions that aresimpler, more seductive, and profoundly more fun than traditionalanalogue therapies or digital treatment regimen. Described more simply,the system and platform curates existing digital content, and createsentirely new content programs, informed by and centered aroundtechniques proven to boost mood, alleviate anxiety, reduce stress, andimprove psychological health or mental fitness by directing users tofollow procedures proven to increase the production of beneficialmolecules and neurotransmitters like Dopamine, Oxytocin, Acetylcholine,Serotonin, and GABA to deliver positive mood and mind-altering effects.This is, in essence, a purely digital, trans-orbital drug deliverysystem. No pills. No powders. Purely digital experiences to positivelyimpact mood, mind and personal sense of well-being.

FIG. 17 illustrates a block diagram representing a system including themood mapper module 308 in relation to the EMS store and prescribers inaccordance with an aspect of the invention. In combination, the systemenables a specific sub-routine that ultimately provisions a dynamicassessment of a user's EMS (dEMS) based on a multi-correlate coordinatesystem (mood map). FIG. 18 illustrates a graphical representation of themood map including the at least two correlates of behavior(active/passive 902; positive/negative 904) underlying the user-plottedassessment of behavior in accordance with an aspect of the invention.The mood map allows users to plot as a single point along at least twocorrelates of behavior—resulting in a push of hyper-personalized digitalcontent with therapeutic value to reinforce or counter the user-mappeddynamic assessment.

In a preferred embodiment, as demonstrated in FIG. 17/18, the systemfeaturing a user-plotted mood map for deriving a dEMS for ahyper-personalized digital therapeutic comprises a message prescriber305, 307; an EMS store 303; a processor coupled to a memory elementstored with instructions, said processor when executing saidmemory-stored instructions, configure a mood mapping module (moodmapper) 308 to cause display of a coordinate-based sentiment valuespectrum (mood map) comprising one positive to negative-scaled axis 904and one perpendicular active to passive scaled axis 902 forming atwo-dimensional plot of a sentiment value along a positive to negativeline (positivity correlate) and an active to passive line (activitycorrelate); at least one user-plotted point on the displayed mood map toreflect a two-dimensional EMS (dynamic EMS) along the two correlates ofpositivity and activity, said dynamic EMS indicating a granularassessment of at least one of a feeling, sensation, mood, mental state,emotional condition, or physical status of the user; and the messageprescriber 305, 307 delivering at least a primary-level message (digitaltherapeutic) personalized to the user based on at least one of a storedmessage coupled to the dynamic EMS (hyper-personalized digitaltherapeutic).

In alternative embodiments, the mood map may comprise at least threeaxis in a three-dimensional representation, wherein one axis representsthe positivity correlate; the second axis the activity correlate; andthe third axis a time or duration correlate. In yet other embodiments,any type of correlates of behavior along any number of axis may berepresented to capture a dEMS based on a user-plot on the mood map.

FIG. 17 illustrates an exemplary processing unit with at least a oneprescriber 305 configured for displaying interactively therapeuticcontent from an EMS store 303 based on a user-plotted dEMS(hyper-personalized digital therapeutics). As shown, the system maycomprise an EMS store 303; at least a primary message prescriber 305; aprocessor coupled to a memory element with instructions, the processorwhen executing said memory-stored instructions, configure the system tocause: at least one EMS from a plurality of EMS in the EMS store 303 tobe pushed based on the user-plotted point, and alternatively, thedynamic sentiment value represented by the user-plotted point based onthe intersecting correlates of behavior.

While not shown in FIG. 17, any number of EMS or EMS types may beincluded in the EMS store 303. Each EMS may indicate at least one of afeeling, sensation, type of discomfort, mood, mental state, emotionalcondition, physical status of the user, and, or a behavioralintervention or training regimen. Any number of messages orinteractively therapeutic content may be associated with each EMS type.Each message; or interactively therapeutic content; or pushedtherapeutic may contain at least one of a text, image, sound, video, artasset, suggested action or recommended behavior. The matching ofmessage; interactively therapeutic content; or pushed therapeutic withEMS type may be pre-defined by at least one of an accredited expert orsource; probabilistic; or deep learned. In a preferred embodiment, anaccredited expert or source will require at least two independentsources of peer-reviewed scholarship or data in order to validate thematch.

The at least primary message prescriber 305 may push a message orinteractively therapeutic content hyper-personalized to the user basedon at least one stored message matched to the selected EMS. For example,within the EMS store 303, if EMS 1 (amused) is selected as plotted inthe far bottom left corner (FIG. 18, 115 c) by the user, any one ofmessage 1, 2 . . . n may be selected by the prescriber 305. Thepre-defined messages validated by the accredited expert may all bemessages with documented utility in elevating mood and energy(counter-effect) or playful, light-hearted (reinforcing). The effectsdocumented for each message may be on a scale. For instance, EMS 1message 1 may be low-moderate; EMS 1/message 2 may be moderate; and EMS1/message n may be high-severe, etc. EMS types or message types may becolor coded or scored to indicate severity. Any variant of the scale maybe featured without departing from the scope of the invention. In otherembodiments, the messages, while falling under the same rubric andun-scaled, can vary along design cues. For instance, the prescriber 305may choose EMS 1/message 2, over other available messages, due to thefact that the message is comprised of traditionally feminine cues(pink-colored bauhaus typeface) for a female user. Other user profile,demographic information, or contextual information may further informthe prescribers 305 choice of message type, such as age, educationlevel, voting preference, etc. User profile or demographic informationmay be user inputted, digitally crawled, sensed or captured.

Still in reference to FIG. 17, the prescriber's 305 choice of messagetype may not be specific to a user, user profile, or crawled user data.In a certain embodiment, the prescriber 305 may have to choose betweenany one of the message types (message 1, message 2 . . . message n) fromthe selected EMS type. This type of message assignment may be completelyarbitrary. In other embodiments, the message assignment may be notspecific to a user-generated or crawled profile but may be based on userhistory. In other words, a user's tracked level of engagement with aprevious message or message from a previous session may inform messageassignment by the prescriber 305. Tracking engagement of a user with apushed or prescribed therapeutic message may be by camera-captured eyegazing, touch-screen interaction, time span between pushed therapeuticand user follow-up action, choice of follow-up action, etc.

In some embodiments, the full list of message types is not grouped byEMS type or along any design categories, but rather simply listedarbitrarily and mapped or matched to an appropriate EMS type. In thisarbitrarily listed manner, the prescriber 305 may match to more than oneEMS type. Likewise, a user may be defined by more than one EMS type andbe prescribed the same message type. For instance, as illustrated inFIG. 18, a plot may indicate for an intermediary EMS between astonishedand indifferent 115 d or between sad and mad 115 e. In such an example,two EMS types may be diagnosed/assigned. Message types may be prescribedsuited for the intermediary EMS diagnosis. For example, in the case ofthe intermediary astonished/indifferent, the plot point may indicatethat the user is stronger leaning towards astonished than indifferent,and as a result, exclude certain message types associated with thetraditional indifferent EMS type. In other embodiments, the mood mappermodule 308 can take the coordinate position of each plot, calculate aprecise position, and plot the positon on the dynamic sentiment valuespectrum, wherein every coordinate position correlates with a preciseEMS. Precise EMS types may be on a scale or depicted with certainleanings/dispositions toward a specific correlate or other EMS types.

FIGS. 19A-19D are exemplary screen shots of the mood map interface inaccordance with an aspect of the invention. In FIG. 19A, the mood mapsx-axis is an activity correlate represented as an ocean surfacehorizontally bisecting the display, whereby a wave action increases thefurther right or left from a center point and the wave action calmsfurther left or right from the center point. The mood maps y-axis is apositivity correlate represented as a sky above the ocean surface andocean depth below the ocean surface, whereby the sky light becomesbrighter (indicating positivity) the further up from the center pointand the ocean depth becomes dimmer (indicating negativity) the furtherdown from the center point (FIGS. 19B, 19C, 19D).

While not illustrated, in some embodiments, the mood maps z-axis is atime or duration correlate, wherein the mood map shifts ninety degreesto reveal a side-sectional view of the ocean surface and shore, wherebythe shore at either end of the display represents day zero and time orduration increases the further from the shore. Other correlates ofbehavior may be represented on any one of the two or three axis of themood map, without departing from the scope of the invention.

The mood map/mapper may allow for a user to plot a single point on thex/y or x/y/z map, wherein each axis represents a unique andcomplementary behavioral attribute. In other embodiments, the user mayplot multiple points on the x/y or x/y/z map to provide at least fourbehavioral attributes to inform a dEMS assessment by creating acoefficient, which may be eventually converted into at least one of adEMS score, dEMS behavioral characteristic, d/EMS type, neurotransmitterimplicated, treatment regimen, digital therapeutic type, etc. In otherembodiments, at least one of a dEMS score, dEMS behavioralcharacteristic, d/EMS type, neurotransmitter implicated, treatmentregimen, or digital therapeutic type may be derived without the need ofa coefficient or algorithmically, statistically, probabilistically, ordeep learned.

In yet other embodiments, the user may engage the mood map/mood mapperby finger-tip scrolling across the map and removing the finger topinpoint the exact location of the circle/cursor point to define the atleast two-axis correlates of behavior. In other embodiments, the usermay finger-tip scroll across the map and double-tap to pinpoint theexact location of the circle/cursor point.

While not illustrated, in some embodiments, the mood map/mood mapper maybe configured in alternate ways to interactively engage the user indefining the user's dEMS. For instance, in one embodiment, themap/mapper may may be an interface comprising a series of verticallyoriented scales requiring the user to slide a bar up or down the scalein response to a question designed to infer wholly or partially a dEMSof the user. For instance, “what is the likelihood of the polar ice capscompletely melting by 2050?” The user would be prompted to slide the barin response to the question, wherein the furthest top of the scalerepresents an extremely high likelihood and the furthest bottom of thescale represents an extremely low likelihood. Questions may extend toquestions of a more personal nature, such as, “do you believe you willlose your patience, exhibited by an outburst of some kind, during thecourse of the work day today?” The system may capture the responses toeach of the questions and create a coefficient, which may be eventuallyconverted into at least one of a dEMS score, dEMS behavioralcharacteristic, d/EMS type, neurotransmitter implicated, treatmentregimen, digital therapeutic type, etc. In other embodiments, at leastone of a dEMS score, dEMS behavioral characteristic, d/EMS type,neurotransmitter implicated, treatment regimen, or digital therapeutictype may be derived without the need of a coefficient oralgorithmically, statistically, probabilistically, or deep learned.

FIG. 20A and FIG. 20B are exemplary screen shots of thehyper-personalized digital therapeutic pushed to the user-plotted dEMS.Once the EMS (regimen) is defined and a particular course of treatment(message) is started, the first card (window) explicitly identifies thespecific EMS and its associated neurotransmitter/effects. While notshown, by tapping the specific EMS and its associatedneurotransmitter/effects, users can see the source of supportingscientific research. By tapping the hamburger icon, users can choose tosave the individual card, or share the card and its contents withfriends across social media. It is to be understood by a person ofordinary skill in the art that these icons, or any icons, on this card(window), or any card (window), may be positioned elsewhere (oranywhere), without departing from the inventive scope.

The focal point of the same or next card (window) in series is theactual EMS-defined message (treatment), and in the case of this window,a suggested action—to plan a day outdoors with a friend. This suggestedaction is to restore/maintain/improve on the dynamic EMS of happiness byexpanding on the associated serotonin neurotransmitter, which isdocumented for building happiness and confidence. The veracity of themessage or suggested action is supported by the referenced peer-reviewedresearch and co-signed credentialed expert. As a person skilled in theart will recognize that modifications and changes can be made to theembodiments of the cards, windows, icons, design elements, EMS types,behavioral intervention types, message types, without departing from thescope of this invention.

While not shown in FIGS. 20A and 20B, the messages (cards/windows) maycomprise a single or battery of physical and, or cognitive tasks andbased on responses, further indicate a more nuanced EMS for a moretailored initial or subsequent (secondary) message. Responses mayinclude a level of compliance, engagement, interaction, choices, etc.Secondary messages may be pushed from secondary prescribers or primaryprescribers coupled to the EMS store. Furthermore, for deeper and morenuanced EMS definition, assigning an indication score or color-codedrange to further convey EMS severity may be achievable. As a result,matching of message type to scored or color-coded EMS may produce a morerefined match for pushing of even more personalized digital content ortherapeutics to boost mood, alleviate anxiety, reduce stress, andimprove psychological health or mental fitness by directing users tofollow procedures proven to increase the production of beneficialmolecules and neurotransmitters like Dopamine, Oxytocin, Acetylcholine,Serotonin, and GABA to deliver positive mood and mind-altering effects.

FIG. 21 illustrates a method flow chart for generating thehyper-personalized digital therapeutic pushed to the user-plotteddynamic EMS. The first step is: selecting at least one EMS for the userbased on a user-plotted point on a displayed mood map to reflect atleast a two-dimensional EMS along at least two correlates of behavior,said EMS indicating a granular assessment of at least one of a feeling,sensation, mood, mental state, emotional condition, or physical statusof the user 1008; and step two entails delivering at least aprimary-level message (digital therapeutic) personalized to the userbased on at least one of a stored message coupled to the EMS 1009.

Map engagement and hyper-personalized digital therapeutic content may bepushed to any number of user devices, such as a smart phone, smartwatch, tablet, smart tv, or any device with a display feature. DynamicEMS (dEMS) assessment via the mood map and content pushing may berendered/delivered identically across the ecosystem of devices. In otherembodiments, the rendering/delivery may be specifically formatted basedon the device form factor/configurations. For instance, a smart watchformat may alter the map presentation, plot mechanisms, and EMS store.The pushed content on a smaller form factor, such as a smart watch, mayfeature more text-based, static imagery, haptic effects, as opposed tolong-form animated or video content. Another user device may feature theuse of a home automation vocal-based hub, configured to recognizenatural based language input and output. In such embodiments, dEMS maybe rendered from vocal tone or responses to targeted question/s. Inother embodiments, dEMS may be user-selected. The hyper-personalizeddigital therapeutic in response to the rendered or user-selected dEMSmay be an audio output of a select EMS type from an audio-based EMSstore. The audio output may comprise at least one of a narrative, sound,song, tune, voice message, etc.

FIG. 22 illustrates a method flow chart for delivering ahyper-personalized digital therapeutic based on a system-plotted EMSparsed from an electronic message. The first step is: selecting at leastone EMS reflecting at least a two-dimensional EMS along at least twocorrelates of behavior, said EMS parsed from an electronic message ofuser 1 (sender) 1102; and step two entails delivering at least a firstmessage (digital therapeutic/digital content) personalized to the userbased on at least one of a stored message coupled to the electronicmessage-parsed EMS from user 1 to at least a second user 1104.

In one embodiment, the electronic message (text input) once parsed, mayaccompany the at least first message (corresponding digitaltherapeutic/digital content/message) by being delivered in tandem,first, or after. In some embodiments, the parsed message mayadditionally have a sentiment vector overlaid (vectorized) and deliveredin tandem, first, or after the matched digital content. At least one ofthe mood map or EMS label from user 1 (sender) may be delivered intandem, first, or after the content. Furthermore, at least the sendermay have the option to waive off at least one of the electronic message(text input), vectorized message, or the first message (digitalcontent).

In one embodiment, user 1 (sender) may value the vectorized message, butnot value the digital content, and choose to waive off the selecteddigital content, and simply send the vectorized message. In otherembodiments, the sender may value the content, but not the vectorizedmessage, and therefore decide to just send the content with the originalelectronic message (text input). In other embodiments, the text input orvectorized message may be waived off and the content alone may bedelivered. In yet other embodiments, the waive settings may bepre-defined by the user for any of the delivery conditions.

In one embodiment, the system plotted mood map indicating the usersmulti-dimensional EMS may be displayed to at least user 1 (sender). Theplotted mood map may be shared with others by user 1, either in-networkor out of network. In one embodiment, user 1 may have the option towaive off the system-plotted EMS parsed from the electronic message andself-plot an EMS point on the mood map. In similar fashion, contentcorresponding to any one of the EMS may be waived off and the user maychoose an alternate content from the prescriber-suggested list or theentire EMS store.

In one embodiment, the at least user 2 (recipient) response may compriseat least one of a raw text input, vectorized message, digital content,digital content paired with vectorized message, digital content pairedwith raw text input, digital content paired with user 2 mood map,digital content paired with user 2 EMS, digital content within the sameEMS group parsed from user 1 input, or digital content based on user 2engagement or compliance. In one embodiment, the at least user 2 mayhave the option to waive any of the receipt or delivery conditions inreal-time or pre-set.

In a preferred embodiment, the system may comprise delivering a digitaltherapeutic, specific to an emotional or mental state (EMS) parsed froman electronic message, comprising: an electronic computing devicecommunicatively coupled to a processor. The processor furthercomprising; an EMS store; a message prescriber; a sentiment vectorgenerator comprising: a parsing module; a coordinate-based sentimentvalue spectrum comprising one positive to negative-scaled axis and oneperpendicular active to passive scaled axis forming a two-dimensionalplot of a sentiment value along a positive to negative line (positivitycorrelate) and an active to passive line (activity correlate). Theprogram executable by the processor and configured to: receive a textinput comprising message content from the electronic computing device;parse, at the parsing module, the message content comprised in the textinput for emotionally charged language, wherein the parsing modulefurther comprises a semantic layer configured to recognize naturallanguage syntax for conversion into a standardized lexicon; based on theemotionally charged language, plot a sentiment value as a point on thecoordinate-based sentiment value spectrum for the text input, whereinthe plotted point reflects a two-dimensional sentiment value along thetwo correlates of positivity and activity for said text input.

Based on the plotted two-dimensional sentiment value, the system selectsat least one EMS from a plurality of EMS in the EMS store, said selectedEMS indicating at least one of a feeling, sensation, type of discomfort,mood, mental state, emotional condition, or physical status of a firstuser; the message prescriber delivering at least a first message(digital therapeutic) personalized to the first user to at least asecond user based on at least one of a stored message coupled to theselected EMS; The at least first message comprises at least one of atext, image, sound, video, art asset, suggested action or recommendedbehavior.

In another embodiment, the system may select at least one EMS from aplurality of EMS in the EMS store and the prescriber may push deliveryof at least one message corresponding to the selected EMS based on theparsed text input without plotting along the at least two correlates ofbehavior on a coordinate (mood map). In another embodiment, parsing thetext input without the use of a mood map or plotting along correlates ofbehavior may be accomplished to ascertain an EMS and a correspondingmessage without vectorizing the text input (vectorized message).

In this embodiment, EMS designation and subsequent message delivery isbased on the particular user submitting an electronic message through amobile application (e.g., a native or destination app, or a mobile webapplication) installed on the particular user's mobile phone or accessedthrough a web browser installed on the user's phone. In one example ofthis embodiment, the user accesses the mobile application, submits theelectronic message in the form of a text input, text converted fromvoice, or simply voice. The sentiment vector generator can then analyzethe message content included within the electronic message, determinethe mood or sentiment of the message content, and apply a correspondingEMS. In some examples, the user can then send at least one of the textinput, vectorized message, EMS, or message, i.e., digitaltherapeutic/content (tvem) to the user's intended recipient(s) (e.g., bycopying and pasting at least one of the above tvem into a separatemessaging application, selecting to export at least one of the abovetvem to a separate application, or directly delivering at least one ofthe above tvem within or external to the application). In one variationof this embodiment, the user may send the tvem message to the intendedrecipient directly through the mobile application through touch input,gesture-based input, or vocal-based input.

In another embodiment, the user may input an electronic message or textinput into an entry field of a third-party application such as an emailclient (e.g., Gmail, Yahoo Mail) or a social media application (e.g.,Facebook, Twitter, Instagram). For example, the user may input a messageinto the body of an email, or into a status update on Facebook. In thisembodiment, the system can detect the input of the electronic messageinto the third-party application and upload the electronic message tothe sentiment vector generator. The sentiment vector generator can thenanalyze the message content contained within the electronic message,determine the mood or sentiment of the message content (EMS), and applyat least one of a corresponding sentiment vector to the electronicmessage (vectorize) or deliver the message (digitaltherapeutic/content), which may be a static image, animated image (GIFformat), video clip, or audio clip.

In an exemplary embodiment, the processing unit with at least a oneprescriber is configured for displaying a message (interactively digitaltherapeutic/digital content) from an EMS store based on a system-plottedEMS based on parsing of an electronic message. Any number of EMS or EMStypes may be included in the EMS store. Each EMS may indicate at leastone of a feeling, sensation, type of discomfort, mood, mental state,emotional condition, physical status of the user, and, or a behavioralintervention or training regimen. Any number of messages orinteractively therapeutic content (digital content) may be associatedwith each EMS type. Each message; or interactively therapeutic content;or pushed therapeutic may contain at least one of a text, image, sound,video, art asset, suggested action or recommended behavior. The matchingof message; interactively therapeutic content; or pushed therapeuticwith EMS type may be pre-defined by at least one of an accredited expertor source; probabilistic; or deep learned. In a preferred embodiment, anaccredited expert or source will require at least two independentsources of peer-reviewed scholarship or data in order to validate thematch.

The message prescriber may push a message or interactively digitaltherapeutic/digital content hyper-personalized to the user based on atleast one stored message matched to the selected EMS. For example,within the EMS store, if EMS 1 (amused) is selected from the parsedelectronic message (text input), any one of message 1, 2 . . . n may beselected by the prescriber. The pre-defined messages validated by theaccredited expert may all be messages with documented utility inelevating mood and energy (counter-effect) or playful, light-hearted(reinforcing). The effects documented for each message may be on ascale. For instance, EMS 1 message 1 may be low-moderate; EMS 1/message2 may be moderate; and EMS 1/message n may be high-severe, etc. EMStypes or message types may be color coded or scored to indicateseverity. Any variant of the scale may be featured without departingfrom the scope of the invention. In other embodiments, the messages,while falling under the same rubric and un-scaled, can vary along designcues. For instance, the prescriber may choose EMS 1/message 2, overother available messages, due to the fact that the message is comprisedof traditionally feminine cues (pink-colored bauhaus typeface) for afemale user. Other user profile, demographic information, or contextualinformation may further inform the prescribers choice of message type,such as age, education level, voting preference, etc. User profile ordemographic information may be user inputted, digitally crawled, sensedor captured.

In one embodiment, the text input is at least one of a SMS, text,e-mail, social media post, text converted from voice, orenterprise-level workflow automation tool message from at least thefirst user that is either overlaid with a sentiment vector (vectorizedmessage) based on the plotted sentiment value and delivered at least oneof before, after, or concurrently with the first message to at least thesecond user or not vectorized and delivered at least one of before,after, or concurrently with the first message to the second user. Forinstance, the user may choose to not vectorize the input and send theraw text input or electronic message in tandem with theprescriber-selected message (digital therapeutic/digital content).

In one instance, the first message delivered from the sender (firstuser) to the recipient (second user) may be at least one of a physicalor cognitive task and based on compliance or performance by the seconduser, push at least one of a text input, vectorized message, mood map,or a follow up message from the sender. For instance, if the recipientresponds quickly by immediately jumping for 5 seconds (sensed by motionsensors embedded on the mobile device) upon a request to “jump for 5seconds” in the first message by the sender, then the sender may delivera follow up message to “chill” unprompted by the recipient or not inresponse to any text input, vectorized message, mood map, EMS, ormessage from the recipient. In another instance, the sender may push afollow up message in succession that are similar to one another (samemessage/EMS type) without taking the recipients physical or digitalreaction or response into account.

In one instance, the first user (sender) may receive at least onemessage from the second user (recipient) in response to at least one ofa text input, vectorized message, EMS, mood map, engagement orcompliance to at least one of the senders text input, vectorizedmessage, EMS, mood map, or first message. As an example, the recipientmay enter the text “wow, that was hard work!” in response to the sendersmessage/digital content and opt to deliver a vectorized message, alongwith a message/digital content. As a result, the text input, “wow, thatwas hard work” may be vectorized with an anthropomorphized dog barking“rough, rough, rough!!!”, followed by a looping gif of a dog chasing asquirrel in circles around a tree.

In another instance, the recipient may opt to deliver the vectorizedmessage in tandem with the message/digital content. As a result, thelooping gif of the dog chasing the squirrel in circles around the treeis overlaid with the vectorized message of the anthropomorphized dogbarking, “rough, rough, rough!!” accompanied with the text, “wow, thatwas hard work!”. In another instance, the text input, “wow, that washard work!” may follow after the looping gif.

In one instance, digital therapeutic/content (message) is in the form ofa card/window, describing the EMS (regimen), followed by a particularcourse of treatment (message). On the top-right portion of the next cardexplicitly identifies the specific drug benefit. By tapping the drugabbreviation, users can see the source of supporting scientificresearch. By tapping the hamburger icon, users can choose to save theindividual card, or share the card and its contents with friends acrosssocial media. It is to be understood by a person of ordinary skill inthe art that these icons, or any icons, on this card (window), or anycard (window), may be positioned elsewhere (or anywhere), withoutdeparting from the inventive scope.

The focal point of the card (window) is the actual EMS-defined message(treatment), and in the case of this window, is a suggested action—jumpfor 5 seconds. Jumping for 5 seconds is a suggested action to restorethe oxytocin neurotransmitter, which is documented for buildinghappiness and confidence—the initially chosen EMS or behavioralintervention by the user. The veracity of the message or suggestedaction is supported by the referenced peer-reviewed research, but neednot be. As a person, skilled in the art will recognize from the previousdetailed description and from the figures and claims, modifications andchanges can be made to the embodiments of the cards, windows, icons,design elements, EMS types, behavioral intervention types, messagetypes, without departing from the scope of this invention as defined inthe following claims.

The messages (digital therapeutic/content) may also be cards/windowsthat may comprise a single or battery of physical and, or cognitivetasks and based on responses, further indicate a more nuanced EMS for amore tailored initial or subsequent message. Responses may include alevel of compliance, engagement, interaction, choices, etc. Furthermore,for deeper and more nuanced EMS definition, assigning an indicationscore or color-coded range to further convey EMS severity may beachievable. As a result, matching of message type to scored orcolor-coded EMS may produce a more refined match for pushing of evenmore personalized digital content or therapeutics. The digitaltherapeutic offerings—with the aid of Artificial Intelligence (AI),machine learning, and, or predictive EMS assessment tools—may deliverincreasingly personalized solutions uniquely tailored to aid eachsubscriber.

At least one of the of the text input, vectorized message, EMS, moodmap, or message, i.e., digital therapeutic/content (tvem) may bedelivered from any one of the users either in tandem, before, or afterthe message specifically tailored to at least one of the parsedelectronic message, engagement or compliance to a message from any ofthe users.

Embodiments are described at least in part herein with reference toflowchart illustrations and/or block diagrams of methods, systems, andcomputer program products and data structures according to embodimentsof the disclosure. It will be understood that each block of theillustrations, and combinations of blocks, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general-purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner such that the instructions stored in the computer-readable memoryproduce an article of manufacture including instruction means whichimplement the function/act specified in the block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus, to produce a computer implemented

process such that, the instructions which execute on the computer orother programmable apparatus provide steps for implementing thefunctions/acts specified in the block or blocks.

In general, the word “module” as used herein, refers to logic embodiedin hardware or firmware, or to a collection of software instructions,written in a programming language, such as, Java, C, etc. One or moresoftware instructions in the unit may be embedded in firmware. Themodules described herein may be implemented as either software and/orhardware modules and may be stored in any type of non-transitorycomputer-readable medium or other non-transitory storage elements. Somenon-limiting examples of non-transitory computer-readable media includeCDs, DVDs, BLU-RAY, flash memory, mobile device, remote device, and harddisk drives.

I claim:
 1. A system for delivering a digital therapeutic, specific toan emotional or mental state (EMS) parsed from an electronic message,the system comprising: an electronic computing device communicativelycoupled to a processor; said processor comprising; an EMS store; amessage prescriber; a sentiment vector generator comprising: a parsingmodule; a coordinate-based sentiment value spectrum comprising onepositive to negative-scaled axis and one perpendicular active to passivescaled axis forming a two-dimensional plot of a sentiment value along apositive to negative line (positivity correlate) and an active topassive line (activity correlate); a program executable by the processorand configured to: receive a text input comprising message content fromthe electronic computing device; parse, at the parsing module, themessage content comprised in the text input for emotionally chargedlanguage, wherein the parsing module further comprises a semantic layerconfigured to recognize natural language syntax for conversion into astandardized lexicon; based on the emotionally charged language, plot asentiment value as a point on the coordinate-based sentiment valuespectrum for the text input, wherein the plotted point reflects atwo-dimensional sentiment value along the two correlates of positivityand activity for said text input; based on the plotted two-dimensionalsentiment value, select at least one EMS from a plurality of EMS in theEMS store, said selected EMS indicating at least one of a feeling,sensation, type of discomfort, mood, mental state, emotional condition,or physical status of a first user; the message prescriber delivering atleast a first message (digital therapeutic) personalized to the firstuser to at least a second user based on at least one of a stored messagecoupled to the selected EMS; and wherein the at least first messagecomprises at least one of a text, image, sound, video, art asset,suggested action or recommended behavior.
 2. The system of claim 1:wherein the parsing module further comprises a heuristic layerconfigured to recognize at least one of shorthand script, symbol, andemotional icon for conversion into a standardized lexicon; and whereinthe sentiment vector generator is further configured to: convert, at theparsing module, the message content comprised in the text input receivedfrom the electronic computing device into converted text in thestandardized lexicon; and parse, at the parsing module, the convertedtext for emotionally-charged language.
 3. The system of claim 1, furthercomprising a neural network communicatively coupled to the processor andconfigured to: access a history of text inputs received from theelectronic device associated with the particular user and parsed by theparsing module; and employ machine learning techniques to dynamicallytrain the parsing module based on the history of text inputs.
 4. Thesystem of claim 1, wherein the parsing module further comprises areference library of emotionally-charged language; and wherein thesentiment vector generator is further configured to: perform areferencing of the emotionally-charged language with the library ofemotionally-charged language; and generate a sentiment value from thedynamic sentiment value spectrum for the text input based on thereferencing of the emotionally-charged language with the library ofemotionally-charged language.
 5. The system of claim 1, wherein the textinput is at least one of a SMS, text, e-mail, social media post, textconverted from voice, or enterprise-level workflow automation toolmessage from the first user that is either overlaid with a sentimentvector (vectorized message) based on the plotted sentiment value anddelivered at least one of before, after, or concurrently with the firstmessage to at least the second user or not vectorized and delivered atleast one of before, after, or concurrently with the first message tothe second user.
 6. The system of claim 1, wherein the sentiment vectorgenerator is further configured to: identify an intended recipient ofthe text input received from the electronic computing device associatedwith the particular user; generate recipient context of the intendedrecipient; and based at least in part on the emotionally-chargedlanguage and the recipient context, generate a sentiment value from thedynamic sentiment value spectrum for the text input.
 7. The system ofclaim 6, wherein the recipient context is an aggregate of at least oneof social media data, IoT data, wearable device data, genetic profiledata, and stress data of the at least second user.
 8. The system ofclaim 1, wherein the sentiment vector generator is further configuredto: generate sender context associated with the first user; and based onat least one of the emotionally-charged language and the sender context,generate a sentiment value from the dynamic sentiment value spectrum forthe text input.
 9. The system of claim 8, wherein the sender context isan aggregate of at least one of social media data, IoT data, wearabledevice data, genetic profile data, and stress data of the first user.10. The system of claim 1, wherein the electronic computing devicefurther comprises a sensor configured to capture sensor data includingat least one of a facial expression, gesture, eye-gaze, respirationrate, heart rate, cortisol level, and motion of the particular user; andwherein the sentiment vector generator is further configured to generatea sentiment value from the dynamic sentiment value spectrum for the textinput based on at least one of the emotionally-charged language and thesensor data.